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Conversion Uplift

Main Effects

Definition

Main effects are the impact of changing a single variable in a multivariate test. When a test only monitors main effects the assumption is made that there is no interaction between individual variables (see Variable interaction).

If this is not the case there is a danger that the winning recipe will not deliver the expected uplift because of the interaction between individual variables. This is a drawback of fractional factorial multivariate tests which assume there is no relationship between the independent variables.

Comparison of Full Factorial and Fractional Factorial DOE

Image of table of comparison between full factorial and fractional factorial design

Resources:

Conversion marketing – Glossary of Conversion Marketing.

Over 300 tools reviewed – Digital Marketing Toolbox.

A/B testing software – Which A/B testing tools should you choose?

Types of A/B tests – How to optimise your website’s performance using A/B testing.

Credit:

https://www.flaticon.com/search?word=testing

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Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.
Neal Cole

Neal Cole

Neal is the founder of Conversion Uplift and the author of the official GA4/GTM audit course for CXL. It's the course much of the industry trains on. He's spent over 20 years in digital analytics, including senior roles in online gaming and financial services, working out where businesses' numbers go wrong and what it costs them. His work now focuses on server-side tracking, GA4 and BigQuery: getting the data right, and being the person who's accountable for it when a decision depends on it.

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